Improving P2P Network Traffic Classification with ML multi-classifiers

	International Journal of P2P Network Trends and Technology (IJPTT)
	© 2014 by IJPTT Journal
	Volume - 4 Issue - 2
	Year of Publication : 2014
	Authors : Haitham A. Jamil , Bushra M. A , Ahmed Abdalla , Ban M. K , Sulaiman M. Nor , Muhammad N. Marsono

Citation

Haitham A. Jamil , Bushra M. A , Ahmed Abdalla , Ban M. K , Sulaiman M. Nor , Muhammad N. Marsono."Improving P2P Network Traffic Classification with ML multi-classifiers". International Journal of P2P Network Trends and Technology (IJPTT), V4(2):60-65 Mar - Apr 2014, ISSN:2249-2615, www.ijpttjournal.org, Published by Seventh Sense Research Group.

Abstract

Machine learning (ML) techniques have been known to be a promising method to classify Internet traffic. These techniques have the capability to detect encrypted communication and unknown traffic. However, the generation of examples, feature selection and classifier design have a significant impact in classification results. This paper proposes approach based on multiple ML classifiers in order to provide a robust model for online P2P Internet traffic classification. The process of validation and analysis were done through experimentation on traces captured from Universiti Technologi Malaysia. The results show that the generation of the training model using ML on P2P classification resulted in a high accuracy, low false negative and low classifying time.

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Keywords

P2P traffic, traffic classification, Machine Learning, Multi-classifiers.